Essential Statistical Concepts for the Quality Professional

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ISBN: 9781439894576 | 1439894574
Cover: Hardcover
Copyright: 5/2/2012

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Many books and articles have been written on how to identify the" root cause of a problem. However, the essence of any root cause analysis in our modern quality thinking is to go beyond the actual problem. This book offers a new non-technical statistical approach to quality for effective improvement and productivity by focusing on very specific and fundamental methodologies as well as tools for the future. It examines the fundamentals of statistical understanding, and by doing that the book shows why statistical use is important in the decision making process.

List of Illustrations	p. xiii
List of Tables	p. xv
Preface	p. xvii
Acknowledgments	p. xxiii
Introduction	p. xxv
What Is Statistics?	p. 1
Deductive and Inductive Statistics	p. 2
Stages of a Sample Study	p. 3
Warning!!!	p. 4
Control the Data	p. 5
Performing Power Analysis	p. 8
Significance Testing	p. 11
Wrong Signs	p. 12
Summary	p. 12
References	p. 13
Selected Bibliography	p. 13
Data	p. 15
Describing Data	p. 16
Data Orientation	p. 17
Nominal, Ordinal, Interval, and Ratio	p. 17
Sampling Considerations	p. 21
Rational Samples	p. 21
Rational Subsamples	p. 22
Rational Sampling Pitfalls	p. 22
Frequency of the Sample	p. 22
Degrees of Freedom	p. 23
Where Do I Start?	p. 24
What Is a p-Value?	p. 25
Assessing Normality	p. 27
Ryan-Joiner Test	p. 28
Summary	p. 28
Selected Bibliography	p. 28
Summarizing Data	p. 31
Frequency Distributions	p. 32
Histograms	p. 33
Why Histograms Are Used	p. 33
Graphical Presentations	p. 34
Dot Plots	p. 34
Stem and Leaf Displays	p. 35
Box Plots	p. 36
Scatter Diagrams	p. 36
Reading Scatter Diagrams	p. 37
Positive Correlation	p. 37
No Relationship	p. 38
Negative Relationship	p. 38
Possible Positive and Negative Correlation	p. 38
Descriptive Statistics	p. 39
The Mean (Average)	p. 39
The Median (Middle)	p. 40
The Mode (Most)	p. 40
Measures of Dispersion	p. 40
Range	p. 40
Standard Deviation	p. 41
Normality	p. 41
Areas in the Normal Distribution	p. 41
Standard Score	p. 42
A Sample from the Normal Distribution	p. 43
Distributions That Aren't Normal	p. 44
The Standard Error of the Mean	p. 44
Tests of Distributions as a Whole	p. 45
Transformations to Obtain Normality	p. 47
Confidence and Sensitivity	p. 47
Test for Independence in Contingency Tables	p. 49
Pearson's Chi-Square Test	p. 50
Calculating the Test Statistic	p. 51
G-Test	p. 51
Fisher Exact Test	p. 52
Barnard's Test	p. 52
Yates' Continuity Correction	p. 53
Summary	p. 53
References	p. 54
Selected Bibliography	p. 54
Tests and Confidence Intervals for Means	p. 59
Test for ¿ when ¿ Is Known	p. 60
Assumptions	p. 60
Normal Test	p. 60
Determination of Sample Size	p. 61
Confidence Intervals and Sample Size	p. 62
Test for ¿ when ¿ Is Unknown	p. 62
Tests for Proportion p	p. 63
Assumptions	p. 63
Test for a Sample of Size n < 150	p. 63
Test for a Sample of Size n > 5/p₀	p. 64
Test for ¿₁-¿₂ when ¿₁ and ¿₂ Are Known	p. 64
Assumptions and Test Conditions	p. 64
Normal Test	p. 65
Confidence Intervals and Determination of Size of Independent Samples	p. 65
Test for ¿₁-¿₂ when ¿₁ and ¿₂ Are Unknown	p. 66
Assumptions and Design of Experiment	p. 66
Test for Paired Observations	p. 67
Test for Two Independent Samples when ¿₁ = ¿₂	p. 67
Test of ¿₁ - ¿₂ = 0 for Two Independent Samples when ¿₁ ≠ ¿₂	p. 67
Test for Differences between Proportions p₁ - p₂	p. 68
Test for Differences among Several Means	p. 69
Detection of a Trend in a Set of Means, with Standard Deviations Unknown but Assumed Equal	p. 69
Summary	p. 70
Selected Bibliography	p. 70
Tests and Confidence Intervals for Standard Deviations	p. 71
Computation and Understanding s and c	p. 72
Chi-Square Test for ¿ in a Normal Distribution	p. 72
Assumptions	p. 72
Test	p. 72
F-Test for ¿₁/¿₂ for Normal Distributions	p. 73
Assumptions	p. 73
Test	p. 73
M-Test for Homogeneity of Variances	p. 74
Assumptions	p. 74
Test	p. 74
Summary	p. 74
Selected Bibliography	p. 75
Tests of Distributions and Means	p. 77
Underlying Distribution Analysis	p. 77
Methods of Testing for Underlying Distributions	p. 79
Probability Plotting	p. 81
Selected Tests	p. 82
Multivariate Statistics	p. 92
Summary	p. 93
References	p. 94
Selected Bibliography	p. 95
Understanding and Planning the Experiment and Analysis of Variance	p. 99
Understanding the Fundamentals of Experimentation	p. 99
Planning the Process	p. 100
Data Analysis versus Statistical Analysis	p. 101
Analysis of Variance	p. 103
Necessary Assumptions	p. 103
Within-Groups Variability	p. 104
Between-Groups Variability	p. 105
Hypothesis Testing	p. 105
Statistical Hypothesis/Null Hypothesis	p. 105
Tests of Hypothesis	p. 106
Hypothesis Testing Steps	p. 108
Calculating the F-Ratio	p. 112
Multiple-Comparison Procedures	p. 112
Interactions	p. 113
How Can You Test the Null Hypothesis That Several Population Means Are Equal?	p. 113
Special Considerations	p. 114
Complete Factorial Design for Treatments	p. 114
Complete Randomization for Background Conditions	p. 114
Randomized Block Design for Background Conditions	p. 115
Latin Square Design for Background Conditions	p. 115
Other Designs	p. 116
Types of Analysis of Variance	p. 116
Type I Problems	p. 116
Type II Problems	p. 116
Taguchi Approach to Design of Experiments	p. 117
Summary	p. 121
References	p. 121
Selected Bibliography	p. 122
Newer Sources	p. 122
Fitting Functions	p. 125
Selecting Dependent Variables	p. 125
Selecting Independent Variables	p. 125
Correlation Analysis	p. 126
Regression	p. 126
Step Regression	p. 128
General Linear Model	p. 130
Discriminant Analysis	p. 131
Log-Linear Models	p. 132
Logistic Regression	p. 132
Factor Analysis	p. 133
Cluster Analysis	p. 134
Testing Hypothesis about Many Means	p. 134
Conjoint Analysis	p. 135
Multiple Regression	p. 136
Partial Regression	p. 137
After Regression	p. 137
Residuals	p. 137
Judging the Size of the Residuals	p. 138,
Checking Assumptions with Residuals	p. 138
Looking for Outliers	p. 139
Normality	p. 139
Prediction and Coefficient Standardization	p. 140
Confidence Intervals for Regression Coefficients	p. 142
Linearity	p. 143
Summary	p. 144
References	p. 145
Selected Bibliography	p. 146
Typical Sampling Techniques	p. 147
Notation and Formula for the Hypergeometric Distribution	p. 148
Binomial Experiment	p. 150
Notation and Formula	p. 150
Binomial Distribution	p. 151
Cumulative Binomial Probability	p. 152
Types of Acceptance Plans	p. 154
Definitions of Basic Acceptance Plans	p. 154
Final Choice Is a Trade-Off Analysis	p. 156
MIL STDs 105D and 114	p. 156
Stratified Sampling	p. 157
Advantages and Disadvantages	p. 157
Proportionate versus Disproportionate Stratification	p. 158
Surveys	p. 158
Quality of Survey Results	p. 159
Sample Design	p. 160
Simple Random Sampling	p. 160
Cluster Sampling	p. 161
Advantages and Disadvantages	p. 161
When to Use Cluster Sampling	p. 162
Difference between Strata and Clusters	p. 162
How to Choose the Best Sampling Method	p. 163
Quality Control Charts and Sampling	p. 165
Attributes or Variables	p. 165
Choice of Plan	p. 166
Inspection Level	p. 166
Summary	p. 167
Selected Bibliography	p. 167
Understanding Computer Programs for Design and Estimating Design Power	p. 169
Examples of ANOVA and ANCOVA Models	p. 169
Types of Statistical Models	p. 173
Strong Recommendations	p. 175
Summary	p. 176
References	p. 176
Epilogue	p. 179
Minitab Computer Usage	p. 181
Formulas Based on Statistical Categories	p. 223
General Statistical Formulas	p. 229
Hypothesis Testing Roadmap	p. 251
Test Indicators	p. 253
Hypothesis Testing-Selected Explanations and Examples	p. 257
When to Use Quality Tools-A Selected List	p. 293
Glossary	p. 305
Selected Bibliography	p. 469
Index	p. 471
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